Difference between revisions of "Syllabus for Machine Learning with Large Datasets 10-605 in Spring 2012"
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* Tues Apr 17. [[Class meeting for 10-605 2012 04 17|Fast KNN and similarity joins 2.]] | * Tues Apr 17. [[Class meeting for 10-605 2012 04 17|Fast KNN and similarity joins 2.]] | ||
* Thus Apr 19. ''no class - Carnival'' | * Thus Apr 19. ''no class - Carnival'' | ||
| − | * Tues Apr 24. | + | * Tues Apr 24. [[Class meeting for 10-605 2012 04 14|SGD for matrix factorization and online LDA]] |
| − | * Thus Apr 26. | + | * Thus Apr 26. [[Class meeting for 10-605 2012 04 16|Scaling up decision tree learning]] |
== May == | == May == | ||
Revision as of 09:08, 27 April 2012
This is the syllabus for Machine Learning with Large Datasets 10-605 in Spring 2012.
Contents
January
- Tues Jan 17. Overview of course, cost of various operations, asymptotic analysis.
- Thus Jan 19. Review of probabilities.
- Tues Jan 24. Streaming algorithms and Naive Bayes.
- New Assignment: streaming Naive Bayes 1 (with feature counts in memory). PDF Handout
- Thus Jan 26. The stream-and-sort design pattern; Naive Bayes revisited.
- Tues Jan 31. Messages and records 1; Phrase finding.
- Assignment due: streaming Naive Bayes 1 (with feature counts in memory).
- New Assignment: streaming Naive Bayes 2 (with feature counts on disk) with stream-and-sort. PDF Handout
February
- Thus Feb 2. More on streaming algorithms: Rocchio, and theory of on-line learning
- Tues Feb 7. More on streaming algorithms: parallelized voted perceptrons.
- Assignment due: streaming Naive Bayes 2 (with feature counts on disk) with stream-and-sort
- New Assignment: phrase finding with stream-and-sort. PDF Handout
- Thus Feb 9. Map-reduce and Hadoop 1 (Alona lecture).
- Tues Feb 14. Map-reduce and Hadoop 2. (Alona lecture, William is closer).
- Assignment due 2/15: phrase finding with stream-and-sort
- New Assignment: Naive Bayes with Hadoop & Phrase-finding with Hadoop PDF Handout
- Thus Feb 16. Hadoop helpers and Scalable SGD
- Tues Feb 21. Scalable SGD and Hash Kernels
- Thus Feb 23. Guest lecture: Ron Bekkerman, LinkedIn, Scaling up Machine Learning
- Tues Feb 28. Background on randomized algorithms; Graph computations 1.
March
- Thus Mar 1. Guest Lecture: Ben van Durme, JHU, Randomized Algorithms for Large-Scale Learning
- Tues Mar 6. Learning on graphs 2.
- Hadoop assignments due
- New Assignment: memory-efficient SGD PDF writeup
- New assignment: initial project proposals. PDF writeup
- Thus Mar 8. Guest Lecture: Joey Gonzales, CMU, GraphLab and Dynamic Asynchronous Computation PPT slides
- Tues Mar 13. no class - spring break.
- Thus Mar 15. no class - spring break.
- Tues Mar 20. Subsampling a graph with RWR
- Assignment due: initial mini-project proposals.
- Assignment due: memory-efficient SGD
- New Assignment: Subsampling and visualizing a graph. PDF writeup
- Thus Mar 22. Semi-supervised learning via label propagation on graphs
- Tues Mar 27. Label propagation 2: Unsupervised label propagation, label propagation as optimization, bipartite graphs
- Assignment due: Subsampling and visualizing a graph.
- New Assignment: mini-project proposals (final version)
- Thus Mar 29. Understanding spectral clustering techniques.
- Assignment due: mini-project proposals (final version).
April
- Tues Apr 3. LDA-like models for text and graphs; guest lecture from Partha Talukdar
- Thus Apr 5. Tentative: Guest lecture by U Kang, CMU.
- Tues Apr 10. Speeding up LDA-like models: sampling and parallelization
- Thus Apr 12. Fast KNN and similarity joins 1.
- Tues Apr 17. Fast KNN and similarity joins 2.
- Thus Apr 19. no class - Carnival
- Tues Apr 24. SGD for matrix factorization and online LDA
- Thus Apr 26. Scaling up decision tree learning
May
- Tues May 1. Project reports.
- Thus May 3. Project reports.
- Fri May 4.
- Project writeups due at 5:00pm. Submit a paper to Blackbook in PDF in the ICML 2012 format (up to 8pp double column), except, of course, do not submit it anonymously.
